Partitioned Narratives: Thick Mapping the 1947 Partition Archive

#Departure locations

part_from <- partition_statistics %>%
  mutate(migrated_from = str_extract(migrated_from, "[^,]+")) %>%
  drop_na(migrated_from) %>%
  select(PersonID, migrated_from, gender)  %>%
  distinct(PersonID, migrated_from, gender) %>%
  add_count(migrated_from, name = "total_location") %>%
  group_by(gender) %>%
  add_count(migrated_from, name = "loc_gender") %>%
  add_count(gender, name = "gender_tot") %>%
  mutate(percent = loc_gender / gender_tot) %>%
  select(-PersonID, -loc_gender, -gender_tot) %>%
  distinct() %>%
  arrange(desc(total_location), gender) %>%
  top_n(5, total_location) %>%
  mutate(percent = percent(percent, 2)) %>%
  select(-total_location)

#Number of departure locations by gender
gender_migration <- partition_statistics %>%
  drop_na(migrated_from) %>%
  distinct(migrated_from, gender) %>%
  group_by(gender) %>%
  count(gender)

partition_transfer <- partition_statistics %>%
  #Filter out Delhi as a final location
  filter(resolved_location != "Delhi") %>%
  
  #Clean up the migrated from and migrated to data
  mutate(migrated_from = str_extract(migrated_from, "[^,]+")) %>%
  mutate(migrated_to = str_extract(migrated_to, "[^,]+")) %>%
  
  #Remove all cases where the migrated from location is the same as one of the transit locations
  filter(migrated_from != resolved_location) %>%
  
  #Remove all cases where the resolved location equals migrated to.
  filter(migrated_to != resolved_location) %>%
  
  #Get the number of transfer locations based on where people migrated from and their gender
  group_by(gender) %>%
  mutate(total_gender = n_distinct(PersonID)) %>%
  group_by(migrated_from, gender) %>%
  add_count(resolved_location, name = "migration_location", sort =
              TRUE) %>%
  #Calculate the percentage as a share of all migration locations
  mutate(percent_transit = migration_location / total_gender) %>%
  
  #Clean up table for presentation
  arrange(desc(percent_transit), resolved_location) %>%
  select(migrated_from,
         gender,
         resolved_location,
         migration_location,
         percent_transit) %>%
  distinct(migrated_from, resolved_location, percent_transit) %>%
  group_by(gender) %>%
  top_n(2, percent_transit) %>%
  mutate(percent_transit = percent(percent_transit, 2)) %>%
  relocate(gender, .before = migrated_from)

#Generate table
partition_transfer %>%
  addHtmlTableStyle(col.rgroup = c("none", "#F5FBFF"),
                    pos.caption = "bottom") %>%
  htmlTable(
    header = c(
      "Gender",
      "Migrated From",
      "Transfer",
      "Percent of Respondents Transfered"
    ),
    caption = "Table 6: Top Transfer Locations by Gender"
  )

hub_from <- partition_statistics %>%
  select(migrated_from) %>%
  drop_na() %>%
  filter(migrated_from != "TBA") %>%
  distinct()

#hub_from_geo <- geo(hub_from$migrated_from, method = 'osm', full_results = FALSE)

hub_from_join <- hub_from_geo %>%
  rename(migrated_from = address)

hub_from_join <- partition_statistics %>%
  left_join(hub_from_join)

hub_from_join <- hub_from_join %>%
  select(name, age, migrated_from, gender, occupation, lat, long) %>%
  filter(migrated_from != "TBA") %>%
  drop_na(migrated_from) %>%
  distinct()

write_csv(hub_from_join,"data/from_hubs.csv")

hub_from_join_clean <- read_csv("data/from_hubs_clean.csv")

hub_from_join_clean <- hub_from_join_clean %>%
  mutate(WKT = paste(
    "LINESTRING(",
    long,
    " ",
    lat,
    ",",
    "",
    "77.2219388",
    "28.6517178)"
  ))
write_csv(hub_from_join_clean, "data/hubs_to_delhi.csv") 

distance_to_border <- read_csv("data/distance_to_border.csv")

#Get the mean distance by gender
group_mean <- distance_to_border %>%
  group_by(gender) %>%
  summarise(grp_mean = mean(distance_km),
            group_median = median(distance_km))

total_mean <- distance_to_border %>%
  ungroup() %>%
  summarise(grp_mean = mean(distance_km))

#Get percentage of people who traveled more than 100km
more_than_100 <- distance_to_border %>%
  mutate(n = n()) %>%
  filter(distance_km > 100)  %>%
  summarise(more_than = n() / n) %>%
  distinct()

distance_to_border %>%
  group_by(gender) %>%
  ggplot(aes(distance_km, fill = gender)) +
  geom_histogram(
    color = "black",
    opacity = .8 ,
    alpha = .4,
    position = "identity"
  ) +
  scale_color_brewer(palette = "Pastel2") +
  scale_fill_brewer(palette = "Pastel2") +
  labs(title = "Histogram of Distance to Border by Gender",
       x = "Distance in km",
       y = "Count",
       fill = "Gender") +
  facet_wrap( ~ gender) +
  theme_classic() +
  geom_vline(data = group_mean,
             aes(xintercept = grp_mean, color = gender),
             linetype = "dashed") +
  theme(legend.position = "none") +
  geom_text(data = group_mean,
            aes(
              x = grp_mean,
              y = 0,
              label = paste("Mean Distance = ", round(grp_mean, 0), "km"),
              hjust = -.05,
              vjust = -22
            ))

to_hubs <- partition_statistics  %>%
  filter(resolved_location != "Delhi") %>%
  mutate(WKT = paste(
    "LINESTRING(",
    longitude,
    " ",
    latitude,
    ",",
    "",
    "77.2219388",
    "28.6517178)"
  ))  %>%
  select(
    name,
    age,
    gender,
    occupation,
    resolved_location,
    migrated_from,
    migrated_to,
    PersonID,
    loc_by_name,
    loc_total,
    WKT
  )
write_csv(to_hubs, "data/hub_and_spoke.csv")

spoke_distance <- read_csv("data/hub_spoke_distance.csv")

distance_spokes_total <- spoke_distance %>%
  filter(migrated_from != "TBA") %>%
  select(name, age, gender, occupation, PersonID, distance_km) %>%
  group_by(name, age) %>%
  mutate(total = round(sum(distance_km))) %>%
  distinct(name, gender, occupation, total)

distance_spokes_summary <- distance_spokes_total %>% 
  ungroup() %>%
  summarise(
    min = min(total),
    #Important the quantile function result has to be converted to a character, because the number does not play nice with htmlTable
    Q1 = as.character(quantile(total, .25)),
    median = median(total),
    Q3 = as.character(quantile(total, .75)),
    max = max(total),
    mean = round(mean(total), 0)
  ) %>%
  relocate(mean, .before = Q3) %>%
  #mean has to be called last because it messes with the resulting values when this is called early on. Everything else becomes mean.
  add_column(value = "Total distance", .before = "min")


distance_spokes_mean <- spoke_distance %>%
  filter(migrated_from != "TBA") %>%
  select(name, age, gender, occupation, PersonID, distance_km) %>%
  group_by(name, age) %>%
  mutate(mean = round(mean(distance_km))) %>%
  distinct(name, gender, occupation, mean) %>%
  ungroup() %>%
  summarise(
    min = min(mean),
    Q1 = as.character(quantile(mean, .25)),
    median = median(mean),
    Q3 = as.character(quantile(mean, .75)),
    max = max(mean),
    mean = round(mean(mean), 0)
  ) %>%
  relocate(mean, .before = Q3) %>%
  add_column(value = "Mean distance", .before = "min")

distribution_table <- distance_spokes_summary %>%
  bind_rows(distance_spokes_mean)

distribution_table %>%
  addHtmlTableStyle(col.rgroup = c("none", "#F5FBFF"),
                    pos.caption = "bottom") %>%
  htmlTable(
    header = c("Value", "Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max"),
    caption = "Table 8: Distribution of Total and Mean distance"
  )

distance_spokes_total %>%
  group_by(gender) %>%
  ggplot(aes(total, fill = gender)) +
  geom_histogram(
    color = "black",
    alpha = .4,
    position = "identity"
  ) +
  scale_color_brewer(palette = "Pastel2") +
  scale_fill_brewer(palette = "Pastel2") +
  labs(title = "Distribution of Total Spoke Length",
       x = "Distance in km",
       y = "Count",
       fill = "Gender") +
  facet_wrap( ~ occupation) +
  theme_classic() 

spokes_mean <- spoke_distance %>%
  group_by(gender, occupation) %>%
  summarise(grp_mean = round(mean(distance_km), 0))

spokes_mean %>%
  addHtmlTableStyle(col.rgroup = c("none", "#F5FBFF"),
                    pos.caption = "bottom") %>%
  htmlTable(
    header = c("Gender", "Occupation", "Mean Total Distance"),
    caption = "Table 7: Mean Total Distance by Gender and Occupation"
  )

spokes_distance_world <- read_csv("data/hub_spoke_distance_international.csv")

spokes_international <- spokes_distance_world %>%
  filter(CNTRY_NAME == "International") %>%
  distinct(PersonID, .keep_all = TRUE)

spokes_domestic <-  spokes_distance_world %>%
  filter(CNTRY_NAME == "Subcontinent") %>%
  anti_join(spokes_international, by = "PersonID") %>%
  distinct(PersonID, .keep_all = TRUE) %>%
  group_by(gender, occupation, CNTRY_NAME) %>%
  count(name = "total")

spokes_international_count <- spokes_international %>%
  group_by(gender, occupation, CNTRY_NAME) %>%
  count(name = "total")

spokes_by_dom_int <- spokes_international_count %>%
  bind_rows(spokes_domestic) 

women_dom_int <- spokes_by_dom_int %>%
  filter(gender == "Female") %>%
  select(occupation, CNTRY_NAME, total) %>%
  ungroup() %>%
  mutate(percent = percent(total / sum(total), 2)) %>%
  select(-gender) %>%
  arrange(occupation)
women_dom_int %>%
  addHtmlTableStyle(col.rgroup = c("none", "#F5FBFF"),
                    pos.caption = "bottom") %>%
  htmlTable(header = c("Occupation", "Region", "Total", "Percent"),
            caption = "Table 9: Women who mention domestic and international locations by occupation")

men_dom_int <- spokes_by_dom_int %>%
  filter(gender == "Male") %>%
  select(occupation, CNTRY_NAME, total) %>%
  ungroup() %>%
  mutate(percent = percent(total / sum(total), 2)) %>%
  select(-gender) %>%
  arrange(occupation)

men_dom_int %>%
  addHtmlTableStyle(col.rgroup = c("none", "#F5FBFF"),
                    pos.caption = "bottom") %>%
  htmlTable(header = c("Occupation", "Region", "Total", "Percent"),
            caption = "Table 10: Men who mention domestic and international locations by occupation")